Jockle: So you were profiled in this report alongside of Numerix and arguably Cubillas has called it the renaissance in market risk management. Some people have called it for the entire rewrite. But as an enterprise data management company, you have an interesting solution to what is vastly becoming the biggest challenge over the next four years. Why don’t you give us a little bit of a description of Xenomorph and the role in which you play with FRTB.

Sentance: Yeah, I guess one of the most fundamental challenges of FRTB is the management of all of the data and analytics and bringing all those together. And so, we feel that we’re uniquely positioned in terms of our expertise in time series, integration with pricing models and analytics from good folks like yourselves to bring all these data sets together to allow the risk managers, the technology guys, to more configure FRTB compliance rather than have to build everything out from scratch. We’re sort of with some of the clients that we’re working with on FRTB at the moment we’re sort of seen in my view, or the feedback received, there’s more of an accelerator to actually get towards compliance.

Jockle: So in looking at this report I think what became clear was from a vendor solution, there isn’t one silver bullet to solve FRTB and that, given the complexity of infrastructures within banks, that more point to point in solving these problems that can adapt to a risk architecture is going to be the critical path forward. In terms of market data management, where are some of the key areas that the clients that are banks that you’re working with, where are they starting? What’s the best starting places in addressing some of these challenges?

Sentance: Well I guess on underpinning the whole of FRTB, it fits within the auspices of BCBS 239 compliance, so that’s a big document about fundamentally about, no pun intended, data management and actually having the data quality and the data lineage and the ability to go back to where did you get that data from. So that’s a foundation that should sort of lie beneath the actual FRTB implementation. But coming to the specifics of FRTB itself, you’ve got a variety of things going on. Most obviously from maybe on the time series side. You’ve got things like modelable and non-modelable risk factors, with some of the requirements to look at how liquid is a particular factor that you’re looking at in the market whether or not it’s got enough and frequent enough observable data. These things like the movement to expected shortfall where you’re dealing with more at the edges of statistical distribution, more than the higher moments of the statistical distribution…

Jockle: With a lot of data sensitivity at that point.

Sentance: Yeah, that’s the big thing. Yeah. Errors on that data, you know, sort of multiply through that process. So that’s there. And then, you know, there’s a variety of challenges. I mean you’ve got all the good work you guys do in the area of XVA. There’s lots of work to be done on PnL attribution; bringing together all of these systems. We’ve been doing a lot of work on not just the management of data within our own market data security management master system, but also the integration of existing databases and the different silos for the different asset classes to try and bring those together. So there’s multitudinous challenges coming up and you know a huge part of it is data, a huge part of it is analytics, but, as you suggested with the start of the question, it’s got to be down to the specifics of the client and you know the situation and the business that that client is in and what’s the best solution.

Jockle: So one of the other questions is always, there’s a timeline. We’re looking at 2019, which means really running things in parallel always starting back in 2018. We’re now coming to the end of 2016. Decisions need to be made. Tell me about, I know from a Numerix perspective, you can look at numerix.com, but from a Xenomorph perspective, tell me about some of the implementation and in terms of working directly with your solution to help onboard and deal with some of these challenges.

Sentance: We’ve done a lot of work in the area as I suggested in terms of the liquidity side of things and looking at time series and building out more out-of-the-box functionality for our clients to look at all of the risk factors that, you know, they’re trying to incorporate into their models and actually be able to classify things a lot quicker and a lot, you know, a lot more straightforwardly than if they were actually starting from scratch, so that’s one area. The data quality side of things is pervasive throughout all of the data sets that are being used, which goes right through from static data through to the market data side of things, the positional data, and your kind of area, the kind of derived data, the kind of calculations that the key part of all of that is got to come together. Again it comes back to your point that there isn’t one out-of-the-box solution for a large institution to solve this problem. You’ve got the have systems that are flexible enough, adaptable enough to be able to support all these different asset classes to be able to support all these different kinds of calculations and indeed all the different types of data involved.

Jockle: Well, Brian thank you so much for joining us today and arguably this is going to be one of the biggest challenges in the market. Analysts have suggested that billions will be spent in terms of solving this new market risk challenge that is FRTB. If you’re interested in a copy of the Celent report, please visit numerix.com and you can download it there. Thank you, Brian.